A method and system for construction progress management of reinforcement of existing building structures

Abstract

The application discloses a kind of existing construction building reinforcement construction progress management method and system, it is related to progress management technical field.The steps of the method include: collecting structure health, construction influence, meteorological environment, operation interference and construction plan data;Data is analyzed using fuzzy comprehensive evaluation method, the degree of risk tendency of structure deterioration and the degree of risk tendency of function interference are calculated, and then the comprehensive risk tendency degree is obtained;If the comprehensive risk tendency degree exceeds threshold value, the construction priority recommendation index of reinforcement process is calculated and sorted, and the recommended construction sequence is obtained;Collect project historical data, calculate weighted average risk index and interference risk duration, if less than allowable delay duration, implement phased progress plan according to recommended sequence;If the interference risk duration exceeds the allowable delay duration, use critical path algorithm to optimize the construction sequence, implement progress optimization scheme.By generating reinforcement construction progress scheme, accurate risk control and efficient progress optimization are realized, and the scientificity and adaptability of management are improved.

Description

Technical Field

[0001] This invention relates to the field of progress management technology, specifically to a method and system for managing the progress of reinforcement construction of existing buildings and structures. Background Technology

[0002] Existing buildings and structures, during long-term service, are prone to gradual structural performance degradation due to factors such as natural material aging, fluctuations in loads, external environmental erosion, and upgraded functional requirements. Some may even develop safety hazards. Therefore, reinforcement construction is necessary to restore structural safety and enhance functionality. Reinforcement construction schedule management, as a core aspect of project control, must not only ensure construction efficiency but also coordinate multiple constraints such as structural safety, operational disruptions, and environmental impacts. This is especially true in scenarios involving continuous operation of buildings or complex surrounding environments, which place stringent demands on the scientific nature and adaptability of schedule management.

[0003] Traditional construction progress management for the reinforcement of existing buildings often adopts an experience-based fixed process sequencing method. This involves determining the logical relationship between each reinforcement process based on construction drawings, engineering specifications, and the past experience of management personnel, and formulating a unified construction schedule plan. Subsequent construction is carried out in sequence according to the pre-set plan, with progress tracking only through periodic on-site inspections.

[0004] However, this method relies solely on experience to formulate fixed reinforcement process plans and proceed according to preset procedures. It cannot capture dynamic data such as changes in structural health status, fluctuations in construction disturbance intensity, and adjustments to operational constraints in real time during construction. As a result, the established process arrangement is difficult to adapt to the actual risk evolution during construction, and it is impossible to dynamically optimize the process sequence and resource allocation in a timely manner. Ultimately, this may lead to delays in key processes, imbalances in resource allocation, or the accumulation and expansion of risks, affecting the overall management and control effect of the project. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for managing the construction progress of existing building reinforcement projects. This method solves the problems that the work sequence arrangement is difficult to adapt to the actual risk evolution during construction and that it is impossible to dynamically optimize the work sequence and resource allocation in a timely manner.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method and system for managing the construction progress of reinforcement of existing buildings and structures, comprising the following steps: Step S1: Collect structural health data, construction impact data, meteorological environment data, operational disturbance data, and construction plan data of existing buildings and structures; Step S2: Analyze the structural health data, construction impact data, and meteorological environment data using the fuzzy comprehensive evaluation method to calculate the structural deterioration risk tendency; analyze the operational interference data, construction impact data, and construction plan data using the fuzzy comprehensive evaluation method to calculate the functional interference risk tendency; and combine the structural deterioration risk tendency and the functional interference risk tendency to calculate the comprehensive risk tendency. Step S3: When the comprehensive risk tendency is greater than the preset threshold, calculate and sort the construction priority recommendation index of each reinforcement process based on the structural deterioration risk tendency and functional interference risk tendency to obtain the recommended construction sequence. Step S4: Collect historical project data, calculate the weighted average risk index based on the recommended construction sequence, and obtain the duration of interference risk by combining the historical project data. Compare the duration of interference risk with the allowable delay duration in the construction plan data. When the duration of interference risk is less than the allowable delay duration, implement the phased schedule according to the recommended construction sequence. Step S5: When the duration of the interference risk exceeds the allowable delay duration, construct a reinforcement process optimization model based on the critical path algorithm, input the recommended construction sequence into the reinforcement process optimization model, output the optimized construction sequence, and then implement the progress optimization scheme of the reinforcement process according to the optimized construction sequence.

[0007] Preferably, the collection of structural health data, construction impact data, meteorological environmental data, operational disturbance data, and construction plan data of existing buildings and structures includes: The multi-source heterogeneous data of existing buildings and structures mainly includes five categories, namely structural health data. Construction impact data Meteorological and environmental data Operational interference data and construction plan data ; Structural health data It is the core of assessing the safety status of a building or structure, and its acquisition depends on on-site investigation and testing; construction impact data The direct effects of reinforcement work on the structure are described, primarily through construction plans and real-time monitoring; meteorological environmental data. This refers to variable factors from the external environment, primarily obtained by connecting to local meteorological monitoring networks or deploying miniature weather stations on-site; operational interference data. This reflects the constraints that buildings impose on construction during their use, stemming from research on the owner's operational model; construction plan data. It is the baseline of project management, provided by the project management documents.

[0008] Preferably, the structural health data, construction impact data, and meteorological environment data are analyzed using fuzzy comprehensive evaluation to calculate the structural deterioration risk tendency; the operational disturbance data, construction impact data, and construction plan data are analyzed using fuzzy comprehensive evaluation to calculate the functional disturbance risk tendency, including: Construct a factor set U and an evaluation set V, and construct a fuzzy relation matrix R. The elements in matrix R... The degree to which index i belongs to evaluation level j is represented by a semi-trapezoidal membership function. For each indicator, based on its numerical value, its membership degree to each risk level is calculated using a preset membership function. Fuzzy synthesis is then performed to obtain the evaluation result B, where each component of B is an element. The calculation formula is as follows: ; in, For comprehensive weighting; For fuzzy operators; This represents the overall membership degree to level v; The fuzzy relation matrix R represents the membership degree of index i to evaluation level v; M is the number of evaluation indicators; and v is the risk level number. Structural deterioration risk tendency Calculated by weighted average: ; in, It is the membership degree of the v-th level in B. It is the assignment of level v. For the number of levels, The value represents the tendency of structural deterioration risk; the higher the value, the more significant the structural deterioration risk. Risk tendency for functional interference According to the degree of structural deterioration risk The same fuzzy comprehensive evaluation calculation method is used to calculate the structural degradation risk tendency degree. The input data is replaced with operational interference data, construction impact data, and construction plan data, while the remaining calculation steps remain unchanged.

[0009] Preferably, the comprehensive risk tendency, calculated by combining the structural degradation risk tendency and the functional disturbance risk tendency, includes: Overall risk tendency By structural deterioration risk tendency and functional interference risk tendency The linear weighted sum is calculated using the following formula: ; in, It is the overall risk tendency; It is the structural degradation risk tendency obtained from the aforementioned calculation; It is the functional interference risk tendency obtained from the aforementioned calculation; α and β are respectively and The combined weighting coefficients.

[0010] Preferably, when the overall risk tendency is greater than a preset threshold, the construction priority recommendation index of each reinforcement process is calculated and ranked based on the structural deterioration risk tendency and functional interference risk tendency, resulting in a recommended construction sequence including: Comprehensive risk orientation of existing buildings and structures When the threshold T is exceeded, the construction priority recommendation index for each reinforcement process is calculated. To optimize the construction sequence; Perform a threshold condition check only if the overall risk tendency is... Subsequent calculations are only triggered when the value exceeds a preset threshold T. Based on the threshold condition being met, a construction priority recommendation index needs to be calculated for each construction reinforcement procedure k. The calculation formula is: ; in, This represents the priority recommendation index for reinforcement procedure k. It is the degree of structural deterioration risk tendency; It is the degree of functional interference risk tendency; and These are the weighting coefficients of reinforcement process k on the risks of structural deterioration and functional disruption, respectively.

[0011] The construction priority recommendation index for all procedures was calculated. Then, the sorting process needs to consider the logical dependencies between processes, and a process dependency matrix D is preset; The recommended construction sequence is obtained by sorting the components according to the process dependency matrix D.

[0012] Preferably, comparing the duration of the disruption risk with the permissible delay duration in the construction plan data, and when the duration of the disruption risk is less than the permissible delay duration, implementing a phased schedule according to the recommended construction sequence includes: The weighted average risk index R is calculated based on the recommended construction sequence, using the following formula: ; Where R represents the weighted average risk index; It is an index of reinforcement processes; This represents the total number of reinforcement procedures. The construction priority recommendation index for reinforcement process k; D represents the estimated duration of reinforcement process k; D represents the total construction duration; weighting coefficients. This indicates the proportion of reinforcement process k in the total construction period; After calculating the weighted average risk index, it needs to be converted into an operational duration indicator, namely, the duration of disturbance risk. It can be calculated using the following formula: ; in, This indicates the duration of the interference risk; R is the weighted average risk index obtained above; This is the risk-time conversion factor.

[0013] Preferably, when the duration of the interference risk exceeds the permitted delay duration, the reinforcement process optimization model is constructed based on the critical path algorithm, including: Construct an optimization model based on the fuzzy critical path method, output the optimal construction sequence, and formulate a specific schedule optimization plan for the reinforcement process accordingly. By introducing fuzzy mathematics theory, and replacing deterministic parameters with six-point fuzzy numbers, the duration of reinforcement process i is represented by a fuzzy number. The total fuzzy project duration is calculated based on fuzzy numbers. ;

[0014] The cost objective function is also fuzzified to obtain the direct cost function. and indirect cost function Then calculate the total fuzzy cost. ;

[0015] Quality is measured using fuzzy confidence level, and a fuzzy confidence level is preset for each reinforcement process. Total fuzzy quality of the project The total fuzzy quality is obtained by aggregating the fuzzy confidence levels of all reinforcement processes. ; The defuzzification method based on the centroid method requires the calculation of fuzzy numbers. The coordinates of the centroid ( , The calculation formula is as follows: ; ; in, It is the x-coordinate of the centroid; It is the ordinate of the centroid; It is a fuzzy number Membership function; Calculate the coordinates of the centroid ( , The Euclidean distance L from the origin (0,0) to the coordinate system. Based on the Euclidean distance L, the centroid coordinates are converted into scalar values, and a multi-objective optimization algorithm is used to solve the model, finally obtaining a Pareto optimal solution set.

[0016] Preferably, the recommended construction sequence is input into the reinforcement process optimization model, and the optimized construction sequence is output, including: The NSGA-II algorithm is used to initialize the population, and chromosomes representing the reinforcement process sequence and duration are randomly generated. Then, non-dominated sorting is performed, and the fitness of individuals is calculated based on the fuzzy objective functions of construction period, cost and quality. The population is then stratified, and the crowding degree of individuals in each non-dominated layer is calculated to maintain the distribution. The population is evolved through genetic operations, and the corresponding crossover probability and mutation probability are set. The algorithm is iteratively run until convergence, and finally, the Pareto optimal solution set is output. Satisfactory solutions can be selected from the solution set based on actual preferences.

[0017] Preferably, the progress optimization scheme for implementing the reinforcement process according to the optimized construction sequence includes: After obtaining the optimized construction sequence, it is necessary to further develop an executable schedule optimization plan for the reinforcement process. This plan should specify the specific time arrangement of each reinforcement process under the optimized sequence. That is, based on the fuzzy start time parameters of each process obtained by model solving, combined with the site resource conditions and calendar time, the specific start time window, resource allocation plan and key control nodes of each process should be determined. The construction party can dynamically adjust the work plan according to this plan and the actual site conditions.

[0018] A construction progress management system for the reinforcement of existing buildings and structures includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0019] This invention provides a method for managing the construction progress of reinforcement of existing buildings and structures, involving machine learning and deep learning technologies, which has the following beneficial effects: (1) The existing building reinforcement construction progress management method is based on the fuzzy hierarchical analysis method to calculate the risk tendency of the building. Traditional methods often have difficulty integrating multi-source heterogeneous data such as the structural health of the building, construction impact, meteorological environment, and operational interference, and cannot fully quantify the risks. However, this method first constructs a multi-level index system that includes the structural deterioration risk and functional interference risk of the building through fuzzy hierarchical analysis, and then uses fuzzy comprehensive evaluation to combine qualitative description with quantitative data to accurately calculate the two types of risk tendency of the building. This can more comprehensively and objectively capture the multi-dimensional risks in the reinforcement construction of existing buildings.

[0020] (2) This existing method for managing the construction progress of building reinforcement effectively overcomes the limitations of a single weighting method by incorporating the entropy weight method into the analytic hierarchy process (AHP) for a combination of subjective and objective weighting. The AHP relies solely on experts' subjective judgment of the importance of indicators. This method obtains subjective weights reflecting expert experience through the AHP, and objective weights based on data distribution through the entropy weight method. Then, it integrates these weights using a multiplicative synthesis method, ensuring that the final comprehensive weights of the indicators conform to the actual engineering logic of building reinforcement construction. This avoids the one-sidedness of a single subjective weighting, significantly improves the rationality of risk assessment indicator weights, and thus ensures the accuracy of risk tendency calculation.

[0021] (3) The existing building reinforcement construction progress management method constructs a reinforcement process optimization model for buildings based on work breakdown structure and CPM critical path method. Traditional methods do not hierarchically decompose reinforcement construction tasks, nor do they identify key reinforcement processes that affect the overall construction period, which can easily lead to confusion in the reinforcement process logic. In contrast, this method refines reinforcement construction tasks into clear and manageable reinforcement process units through work breakdown structure, clearly sorts out the hierarchical relationship and scope of each reinforcement process; and combines it with CPM critical path method to accurately locate the core reinforcement processes that play a decisive role in the overall construction period, avoiding the blind progress control caused by the extensive management of reinforcement processes in traditional methods, and improving the accuracy and efficiency of reinforcement process optimization.

[0022] (4) The existing building reinforcement construction progress management method adds multi-objective evolutionary algorithm, fuzzy number modeling and fuzzy weight to the reinforcement process optimization model. Traditional reinforcement process optimization is mostly guided by a single objective and does not consider the uncertainty of reinforcement construction parameters. The optimization scheme is easy to deviate from the multiple constraints and variable fluctuations in actual reinforcement construction. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for managing the construction progress of reinforcement of existing buildings and structures proposed in this invention.

[0024] Figure 2 This invention provides a hierarchical diagram of the time of interference risk in a method for managing the construction progress of reinforcement of existing buildings and structures.

[0025] Figure 3 This is a hierarchical diagram of the progress plan obtained in the existing building reinforcement construction progress management method proposed in this invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figure 1-3 This invention provides a technical solution: a method and system for managing the construction progress of existing building reinforcement projects. Specifically, the method and system for managing the construction progress of existing building reinforcement projects are provided below. Figure 1 The method includes the following steps: Step S1: Collect structural health data, construction impact data, meteorological environment data, operational disturbance data, and construction plan data of existing buildings and structures.

[0028] This step requires the collection and preprocessing of multi-source heterogeneous data. This is the data foundation of the entire intelligent management method for the construction progress of existing building reinforcement projects. The aim is to build a dataset that comprehensively reflects the current status of the building, the various impacts it is subject to, and the construction plan, providing accurate input for subsequent risk quantification and schedule optimization. The multi-source heterogeneous data of existing buildings mainly includes five categories, namely structural health data... Construction impact data Meteorological and environmental data Operational interference data and construction plan data The specific acquisition process is as follows: Structural health data This is the core of assessing the safety status of a building or structure, and its acquisition relies on on-site investigation and testing. Specifically, it includes: concrete strength data measured using the rebound method; concrete uniformity indicators assessed using the ultrasonic rebound combined method; information on the distribution of main reinforcement bars and the thickness of the protective layer obtained by scanning with a rebar detector; and detailed investigation records of existing cracks (such as width, depth, and direction) and the degree of rebar corrosion. These data collectively characterize the structure's "health status," forming the basis for determining its ability to withstand construction disturbances.

[0029] Construction impact data This describes the direct effects of reinforcement work on the structure, primarily obtained through construction planning and real-time monitoring. Specifically, this includes: the theoretical load values ​​corresponding to the planned reinforcement techniques (such as increasing the cross-section or bonding carbon fiber fabric); vibration data generated by construction machinery (such as drilling equipment); and stress changes and deformation data monitored in real time by sensors deployed at critical locations (such as columns and beams). These data are crucial for assessing the immediate impact of construction activities on structural safety.

[0030] Meteorological and environmental data This refers to variable factors from the external environment, primarily obtained through connections to local meteorological monitoring networks or the deployment of miniature weather stations on-site. Key parameters include: ambient temperature and humidity, which affect concrete curing and the hardening of certain adhesives; precipitation and wind speed, which relate to the scheduling and safety of outdoor work; and the probability of potential extreme weather events in more detailed hourly weather forecasts. These factors directly impact construction efficiency and risk.

[0031] Operational interference data This reflects the constraints that buildings impose on construction during their use, stemming from research into the owner's operational model. It includes: the permissible noise and vibration limits for different functional areas (such as office and commercial areas) during specific time periods (such as weekday daytime and nighttime), and the requirement to ensure unobstructed flow of goods and people to maintain basic operations. These constraints determine the available time window and workspace for construction.

[0032] Construction plan data This is the baseline for project management, provided by the project management document. It specifically defines: a detailed breakdown of all reinforcement procedures and their theoretical sequential relationship, the estimated duration of each reinforcement procedure, the overall allowable project duration, and the maximum permissible delay for critical reinforcement procedures. This serves as the benchmark for measuring schedule deviations and for optimization.

[0033] The collected raw data needs to undergo rigorous preprocessing to eliminate errors, standardize data, and make it suitable for subsequent mathematical model analysis. This process mainly includes data cleaning, transformation, and integration. The specific preprocessing steps are as follows: The first step is data cleaning, which aims to identify and address outliers, missing values, and obvious errors in the raw data. For example, for structural health data... In concrete strength measurements, if the value at a certain measuring point significantly deviates from the regional average without a reasonable structural explanation, it can be considered an outlier. In this case, statistical methods can be used, such as calculating the mean μ and standard deviation σ of all measuring point data. Data points whose values ​​are outside the interval (μ-2σ, μ+2σ) can be identified as outliers and removed or smoothed. For minor data gaps caused by temporary sensor malfunction, linear interpolation of adjacent data can be used to fill in the gaps based on the time series characteristics, ensuring the continuity of the data sequence.

[0034] Next comes data transformation and standardization, the purpose of which is to unify data of different dimensions and magnitudes to a comparable scale. For numerical features, such as construction impact data... The noise limits in vibration data and operational disturbance data differ significantly in magnitude. A min-max normalization method is used to map them to the [0,1] interval. The calculation process is as follows: for a certain feature X, its normalized value... The calculation formula is as follows: ; in, and These are the minimum and maximum values ​​of the feature across all samples, respectively. This approach effectively prevents features with large numerical ranges from dominating the model.

[0035] Finally, there is data integration, which combines five types of data from different sources and in different formats, namely structural health data. Construction impact data Meteorological and environmental data Operational interference data and construction plan data By associating and integrating data using unified timestamps and spatial identifiers, a structured, multi-dimensional time-series dataset is formed. This complete and clean dataset is the reliable input required for risk tendency calculation in step S2.

[0036] Step S2: Use fuzzy comprehensive evaluation method to analyze the structural health data, construction impact data, and meteorological environment data to calculate the structural deterioration risk tendency; use fuzzy comprehensive evaluation method to analyze the operational interference data, construction impact data, and construction plan data to calculate the functional interference risk tendency; and combine the structural deterioration risk tendency and the functional interference risk tendency to calculate the comprehensive risk tendency.

[0037] The purpose of this step is to calculate the structural deterioration risk tendency and functional disturbance risk tendency of existing buildings and structures. The structural deterioration risk tendency is assessed based on structural health data, construction impact data, and meteorological environmental data, while the functional disturbance risk tendency is assessed based on operational disturbance data, construction impact data, and construction plan data. This method uses a combination of weighting methods, the Analytic Hierarchy Process (AHP), and entropy weighting to determine the weights of the indicators, and then applies the Fuzzy Comprehensive Evaluation (FCE) method for comprehensive evaluation to obtain the risk tendency value. The risk tendency is a comprehensive score; a higher value indicates a higher risk level, providing a quantitative basis for risk management.

[0038] To address the risk of structural deterioration, key indicators are extracted from structural health data, construction impact data, and meteorological environmental data to form a multi-level evaluation system.

[0039] The evaluation system of this method is based on the standardized data after preprocessing in step S1, and the following specific evaluation indicators are constructed: The first is the structural deterioration risk indicator system, which is used to calculate the tendency of structural deterioration risk. The primary and secondary indicators are defined as follows: Structural health status, which reflects the initial safety status of the building or structure, includes: crack development coefficient, the larger the value, the more dangerous the crack condition; material strength degradation rate, the larger the value, the more serious the material performance degradation; and steel corrosion rate, which is assigned a grade based on the corrosion survey results. Construction disturbance intensity, this index quantifies the immediate impact of construction activities on the structure, including vibration impact index and load change rate; Environmental sensitivity, this indicator assesses the impact of the external environment on construction safety, including: temperature fluctuation coefficient, with a baseline temperature difference of 20°C; rainfall impact factor, assigned a value based on the forecast rainfall level.

[0040] Next is the functional interference risk indicator system, which is used to calculate the functional interference risk tendency. Its indicators are defined as follows: Operational constraint intensity, this indicator reflects the restrictions that normal operation of buildings and structures imposes on construction, including: noise constraint violation degree and passage occupancy impact, scored according to the degree of impact of key passage occupancy by construction on logistics and pedestrian flow; Construction schedule pressure, an indicator used to assess the tightness of the construction schedule, includes: schedule urgency index, the lower the value, the tighter the schedule; and reinforcement process logic complexity, which is based on the construction schedule network diagram and is measured by the ratio of the number of reinforcement processes on the critical path to the total number of reinforcement processes.

[0041] First, subjective weights are calculated using the Analytic Hierarchy Process (AHP). AHP is used to determine the subjective weights of indicators, comparing their importance based on expert judgment. Constructing the judgment matrix is ​​a crucial step, where each element represents the importance ratio of any pair of indicators. For a set of indicators, the elements of the judgment matrix E... The importance of index i relative to j is represented by a 1-9 scale. Weights are calculated using the eigenvector method, first normalizing each column of the judgment matrix, as shown in the following formula: ; in, These are the elements of the original matrix, representing the importance of index i relative to index j; This represents the importance of indicator k relative to indicator j, where k is the summation index and n is the number of evaluation indicators. These are the normalized matrix elements. Then, the weight vector is obtained by summing the rows and normalizing. The formula is as follows: ; in, This is the subjective weight of the i-th indicator, whose value is directly obtained through matrix operations, relying on the judgment matrix of expert scores. Simultaneously, the consistency of the judgment matrix needs to be verified to ensure logical rationality. The consistency index (CI) is calculated using the following formula: ; in, It determines the largest eigenvalue of the matrix, obtained by solving the characteristic equation; m is the matrix order. The consistency ratio (CR) is calculated using the following formula: ; in, CR is the consistency ratio, used to judge logical consistency; RI is the random consistency index, whose value is obtained from a standard table based on the matrix order m (for example, RI=0.58 when m=3). When CR<0.1, the matrix passes the consistency test, indicating that the weight allocation is reasonable.

[0042] Subsequently, the entropy weight method is used to calculate objective weights. This method calculates objective weights based on the degree of data dispersion, which can reduce subjective bias. Let there be N samples (e.g., historical projects or monitoring time points) and M evaluation indicators. First, the original data matrix is ​​standardized. For positive indicators, the standardization formula is: ; in, It is the original data value of the i-th sample on index j; and These are the minimum and maximum values ​​of index j, respectively; These are standardized data values, ranging from [0,1]. After standardization, the entropy value of each indicator is calculated. The formula is as follows: ; ; in, It is the information entropy of the j-th indicator; It represents the proportion of the i-th sample value in indicator j; N is the total number of samples; k is a constant, k To ensure It falls within the interval [0,1]. Entropy value. Reflecting the uncertainty of the indicators, A smaller value indicates a greater amount of information contained in the indicator. (Coefficient of Difference) The calculation formula is: ; in, It is the difference coefficient of the j-th indicator. The higher the value, the more important the indicator, and the greater its role in the evaluation. Finally, objective weighting. The difference coefficient was obtained through normalization: ; Among them, parameters It is the objective weight of the j-th indicator, and its value is entirely based on the data distribution without the need for human intervention.

[0043] To comprehensively balance expert experience and data objectivity, a multiplicative synthesis method was used to integrate the subjective weights determined by the AHP method. Objective weights determined by the entropy weight method The comprehensive weight of the j-th indicator Calculate using the following formula: ; in, It is the comprehensive weight of the j-th indicator; It is the subjective weight of AHP; This is the objective weighting method based on entropy weights. This formula ensures that the weight product is normalized, resulting in a comprehensive weighting. It combines the reliability of expert experience with the objectivity of data-driven approaches to improve the reliability of assessments.

[0044] Finally, the fuzzy comprehensive evaluation method is used to calculate the structural deterioration risk tendency degree. The fuzzy comprehensive evaluation method is used to comprehensively evaluate the risk level. First, a factor set U (containing all indicators) and an evaluation set V (such as risk levels divided into low, relatively low, average, relatively high, and high, with corresponding value vectors T=[50,60,70,80,90]) are constructed.

[0045] Construct a fuzzy relation matrix R, where the elements of matrix R are... This represents the degree to which index i belongs to evaluation level j (i.e., membership degree). This invention uses a semi-trapezoidal membership function to determine the element... .

[0046] The semi-trapezoidal membership function quantifies the degree of ambiguity in whether an indicator value belongs to a certain risk level. It is composed of ascending and descending semi-trapezoidal functions. For an evaluation set with five risk levels (low, lower, moderate, higher, and high), the membership function for each indicator needs to be defined separately for each level. The key parameters in the function are the upper and lower bounds of the trapezoid; for example, for the "low" risk level, an upper bound for complete membership needs to be defined. and the lower bound where the membership degree drops to 0 These parameters , ,..., Each evaluation indicator needs to be pre-set based on its characteristics. The setting basis may include industry standard limits, historical data statistical characteristics, or domain expert experience.

[0047] For the "low" risk level (v=1), the descending half-trapezoidal function is used, and its membership degree is calculated as follows: ; in, It is the degree of membership that the indicator value is x and belongs to the "low" risk level; It is the upper limit of the "low" risk level, when the indicator value x is less than or equal to At that time, it completely belonged to that level; It is the lower bound of the "lower" risk level, when x is greater than or equal to When x is between [a certain value], it does not belong to the "low" level. and When the membership degree is between 1 and 0, the membership degree decreases linearly from 1 to 0.

[0048] For intermediate risk levels (v=2,3,4), the trapezoidal function is used, and its membership degree is calculated as follows: ; in, It is the membership degree of the vth risk level when the indicator value is x; These are the starting and ending points of the change in membership level, respectively; and This is the core interval of this level; when x is within this interval, it is a complete membership (membership degree is 1). The parameters must satisfy... = This is to ensure a smooth transition between levels.

[0049] For the "high" risk level (v=5), the ascending semi-trapezoidal function is used, and its membership degree calculation formula is as follows: ; in, It is the degree of membership to the "high" risk level when the indicator value is x; It is the upper limit of the "higher" risk level, and also the starting point for the degree of membership to begin to increase; It is the lower bound of the "high" risk level, when x is greater than or equal to When x is completely subordinate. and When the membership degree is between 0 and 1, the membership degree increases linearly from 0 to 1.

[0050] For each indicator, its membership degree to each risk level is calculated based on its numerical value using the aforementioned preset membership function. For example, for the indicator "crack development coefficient," its risk level threshold can be set according to the specifications to determine the values ​​of each parameter 'a'. Then, fuzzy synthesis is performed to obtain the evaluation result B, where each component of B is an element. The calculation formula is as follows: ; in, For comprehensive weighting; For fuzzy operators, a weighted average method is used; This represents the overall membership degree to level v; Let M represent the membership degree of index i in the fuzzy relation matrix R to evaluation level v; M is the number of evaluation indicators; and v is the risk level number. Ultimately, the structural deterioration risk tendency degree... Calculated by weighted average: ; in, It is the membership degree of the v-th level in B. This is the assignment of the level v (e.g., low = 50, higher = 80). Number of levels. The value represents the tendency of structural deterioration risk; the higher the value, the more significant the structural deterioration risk.

[0051] Risk tendency for functional interference Calculation method and structural deterioration risk tendency The calculation method is completely consistent, specifically: calculating the tendency of structural degradation risk. The structural health data, construction impact data, and meteorological environmental data used at the time were replaced with operational disturbance data, construction impact data, and construction plan data. The remaining steps, weight calculation methods, and synthesis methods of the fuzzy comprehensive evaluation were all aligned with the structural deterioration risk tendency. The calculation process is the same.

[0052] Finally, calculate the overall risk tendency. Comprehensive risk tendency The aim is to integrate structural deterioration risk and functional interference risk to form a single quantitative assessment of the overall project risk level, providing a concise basis for global decisions regarding subsequent construction sequence adjustments. (Comprehensive Risk Tendency) By structural deterioration risk tendency and functional interference risk tendency The linear weighted sum is calculated using the following formula: ; in, It is the overall risk tendency, and the higher the value, the higher the overall risk level faced by the project; It is the structural degradation risk tendency obtained from the aforementioned calculation; It is the functional interference risk tendency obtained from the aforementioned calculation; α and β are respectively and The combined weighting coefficients satisfy 0 ≤ α ≤ 1, 0 ≤ β ≤ 1, and α + β = 1. The determination of weighting coefficients α and β depends on the specific requirements of the project. If the project prioritizes structural safety, α should be relatively larger; if the project prioritizes operational assurance, β should be relatively larger. Typically, weights can be assigned based on expert consultation or by analyzing the regression relationship between the two types of risks and their impact on the overall project duration and cost in historical projects. For example, by soliciting 5-7 domain experts to compare the pairwise importance of the two types of risks, a judgment matrix can be constructed to solve for the eigenvectors and obtain the weights, ensuring that the weight allocation aligns with the actual priority of the project.

[0053] The core function of this step is to quantitatively assess the structural deterioration risk and functional interference risk of existing buildings by integrating subjective and objective weighting methods and combining them with fuzzy mathematics theory. This transforms complex, multi-source data into intuitive risk tendency values. This step not only provides a scientific basis for risk assessment but also balances the influence of expert experience and data-driven approaches through a combined weighting method, enhancing the reliability of the results. It also lays a solid foundation for the formulation of risk control measures and decision optimization in the following sections, ensuring that subsequent analyses can prioritize and intervene in a targeted manner based on accurate quantitative indicators.

[0054] Step S3: When the comprehensive risk tendency is greater than the preset threshold, calculate and sort the construction priority recommendation index of each reinforcement process based on the structural deterioration risk tendency and functional interference risk tendency to obtain the recommended construction sequence.

[0055] The core function of this step is to assess the overall risk tendency of existing buildings and structures. When the threshold T is exceeded, the construction priority recommendation index for each reinforcement process is calculated. This method optimizes the construction sequence to effectively manage construction processes in high-risk scenarios. To ensure the feasibility of the construction sequence, this method enforces the consideration of the technological dependencies between processes during sequencing (e.g., certain processes can only begin after the preceding processes are completed), thereby achieving a balance between risk control and construction logic, and avoiding new safety issues arising from the pursuit of risk mitigation.

[0056] First, a threshold condition check is performed; only when the overall risk tendency is... Subsequent calculations are only triggered when the threshold value is greater than T. The preset threshold T is typically determined based on industry standards, historical data, or expert consensus, and its specific value needs to be adjusted according to the actual engineering situation. The comprehensive risk tendency calculated in step S2 reflects the overall risk level of the building; T is a preset threshold, representing the critical point of acceptable risk level, which is determined based on the project background, such as by setting it through statistical historical accident data. If the conditions are met, the calculation continues; otherwise, there is no need to adjust the construction sequence.

[0057] Based on the threshold condition being met, a construction priority recommendation index needs to be calculated for each construction reinforcement procedure k. This index is derived by weighting and combining the directional tendencies of two types of risks to quantify the urgency of reinforcement procedures. The weighting is based on the sensitivity of the reinforcement procedures to the two types of risks, ensuring that the index reflects the actual risk contribution. Construction Priority Recommendation Index The calculation formula is: ; in, This represents the priority recommendation index for reinforcement process k. The higher the value, the more priority the reinforcement process should be given to construction. It is the degree of structural deterioration risk tendency; It is the degree of functional interference risk tendency; and Let K be the weighting coefficients of reinforcement process k on the structural deterioration risk and functional interference risk, respectively, satisfying 0 ≤ K. ≤1、0≤ ≤1 and + =1. These weights are calculated by organizing 5-7 domain experts (structural engineers, construction managers, etc.) to conduct multiple rounds of back-to-back scoring using the Delphi method. The sensitivity of each reinforcement process to the two types of risks is evaluated on a scale of 1-9, and a judgment matrix is ​​constructed to calculate the weights. Weight coefficients and The determination of the reinforcement process needs to take into account its characteristics. For example, the foundation pit excavation process may be more sensitive to the risk of structural deterioration. The components are relatively large, and the equipment installation process may be more sensitive to the risk of functional interference. The formula is relatively large. It transforms the macro-risk trend into an index at the reinforcement process level through linear weighting, taking into account both the overall risk and the differences in reinforcement processes.

[0058] The construction priority recommendation index for all procedures was calculated. Then, the sorting process needs to consider the logical dependencies between processes. Therefore, it is first necessary to define the predefined process dependencies. A process dependency matrix D is predefined, whose elements... Indicates whether process i is a direct preceding process of process j (i.e., process j can only begin after process i is completed), element The preset rules are as follows: ; Matrix D can be obtained by analyzing construction drawings, process flow diagrams, or by consulting construction experts. It depicts the hard logical constraints that cannot be violated in the process network.

[0059] Next, a dependency-constrained sequencing process is performed. This process consists of two steps: First, a topological sort is performed based on the dependency matrix D to obtain a basic sequence of operations that satisfies all logical dependencies. The topological sort ensures that any operation is scheduled only after all its preceding operations have been completed. Second, within the basic sequence framework determined by the topological sort, for operation groups that do not have strict dependencies and can be arranged in parallel or flexibly, their construction priority recommendation index is used. The values ​​are sorted within the group from highest to lowest. For example, if topological sorting determines that processes A and B must both be completed before process C can proceed, but there is no dependency between processes A and B, then the construction priority recommendation index of A and B is compared. Value, priority construction, priority recommendation index High-value processes.

[0060] The final recommended construction sequence is one that both conforms to the technological constraints between processes and prioritizes high-risk processes as much as possible. For example, even the construction priority recommendation index for the foundation pit excavation process is... The value is high, but the priority recommendation index for the construction procedures of the support engineering on which it depends is high. The value is relatively low, and in the final order, the support works will inevitably be placed before the foundation pit excavation. Within the support works, if there are multiple independent sub-processes, their respective construction priority recommendation indices will be used. The order of priority is determined by the value. The entire S3 process, through the introduction of dependency constraints, transforms the risk assessment results into technically feasible and risk-optimized decision support.

[0061] Step S4: Collect historical project data, calculate the weighted average risk index based on the recommended construction sequence, and obtain the duration of interference risk by combining the historical project data. Compare the duration of interference risk with the allowable delay duration in the construction plan data. When the duration of interference risk is less than the allowable delay duration, implement the phased schedule according to the recommended construction sequence.

[0062] The core function of this step is to calculate a weighted average risk index based on the recommended construction sequence generated in step S3, quantify the duration of disturbance risks by combining this index with historical project data, and then compare it with the permissible delay duration in the construction plan to determine whether to implement a phased schedule. This step achieves the transformation from risk assessment to construction decision-making, ensuring that the construction plan is optimized and executed under the premise of controllable risks.

[0063] First, a weighted average risk index is calculated based on the recommended construction sequence. This index aggregates the risk impact of each reinforcement process, reflecting the overall risk level of the reinforcement sequence, and its value has a time dimension. The formula for calculating the weighted average risk index R is: ; Where R represents the weighted average risk index, the higher the value, the greater the overall risk delay potential; This is the reinforcement process index, from 1 to... ; This is the total number of reinforcement procedures, determined by the construction plan; The construction priority recommendation index for reinforcement process k in step S3 represents the risk urgency of the reinforcement process and is derived from the output of step S3. The estimated duration of reinforcement step k is obtained from a pre-set construction information database. This database may contain a list of reinforcement steps, historical duration data, or standard working hours. This database is typically built based on an engineering database or historical project data, and may include reinforcement step attribute tables or resource allocation records. D represents the total construction duration, calculated as D = That is, the sum of the durations of all reinforcement processes; weighting coefficient This indicates the proportion of reinforcement process k in the total project duration, ensuring that the weighted average of risk levels more closely reflects the actual impact.

[0064] After calculating the weighted average risk index, it needs to be converted into an operational duration indicator, namely, the duration of disturbance risk. It represents the construction delay time that may be caused by risk factors. Calculated using the following formula: ; in, The duration of the disruption risk represents the potential construction delay due to the risk factors; R is the weighted average risk index obtained above. The risk-time conversion factor is a conversion factor with a "time / dimensionless" dimension. Its physical meaning represents the expected delay time corresponding to a unit of risk index. It is determined based on regression analysis of actual delay data from similar historical projects and theoretical risk values. For example, it involves collecting data from 20 completed projects. and actual delay time Establish a linear regression model = +b, take the regression coefficient as The value is given by b, where b is the intercept term, representing the base delay time caused by non-risk factors.

[0065] After obtaining the duration of the disruption risk, compare it with the permissible delay duration in the construction plan data. Comparison is needed. Permitted delay duration refers to the maximum allowable delay time in the construction schedule, typically obtained directly from project contracts, regulatory requirements, or planning documents. The comparison criteria are: if... < If the duration of the disruption risk is less than the permissible delay duration, the phased schedule can be implemented directly; otherwise, subsequent reinforcement procedures need to be optimized. A phased schedule refers to breaking down the construction process into multiple stages to reduce the risk concentration in a single stage. For example, high-risk reinforcement procedures can be carried out first, followed by low-risk reinforcement procedures. Details of the plan can be referenced from databases or standard construction manuals.

[0066] Step S4, through risk quantification and comparison, translates theoretical risk assessments into practical construction instructions, enhancing the responsiveness and adaptability of project management. The final output is the selection of a construction plan, directly guiding on-site operations and reducing losses caused by uncertainty.

[0067] Step S5: When the duration of the interference risk exceeds the allowable delay duration, construct a reinforcement process optimization model based on the critical path algorithm, input the recommended construction sequence into the reinforcement process optimization model, output the optimized construction sequence, and then implement the progress optimization scheme of the reinforcement process according to the optimized construction sequence.

[0068] The core of this step is to activate the dynamic reinforcement process optimization module when the duration of interference risk exceeds the permissible delay duration. This module takes the recommended construction sequence generated in step S3 as its core input, and constructs an optimization model based on the fuzzy critical path method with multiple objectives such as schedule, cost, and quality to handle uncertainties in construction. Finally, it outputs an optimized construction sequence and formulates a specific schedule optimization plan for the reinforcement process based on it.

[0069] First, to improve the model's resilience to uncertainty, fuzzy mathematics theory is introduced, and six-point fuzzy numbers are used to replace deterministic parameters. The duration of reinforcement step i is represented by the fuzzy number: ; in, This represents the lower bound of the most likely time, indicating the most likely start time under ideal conditions; This represents the maximum possible timeframe, indicating the most likely point in time to end under ideal conditions. It is a pessimistic time estimate, which represents a pessimistic estimate of the completion time under unfavorable conditions. It is the lower limit of the most likely range, which is an acceptable but not ideal time. It is an optimistic time estimate, which represents an optimistic estimate of the completion time under favorable conditions. It is the upper limit of the most likely range and is an acceptable and relatively ideal time. and These represent the absolute lower bound and absolute upper bound, respectively, meaning the reinforcement process must not be shorter or longer than these values, usually determined based on extreme conditions. This representation method characterizes the uncertainty of the parameters through membership functions, which take a piecewise linear form within the interval [...]. , Linearly increasing on [ ], in [ , The value continues to increase to its peak value. , Maintain peak value on [ ], at [ , Linearly decreasing on [ ], in [ , The time objective function based on fuzzy numbers continues to decrease. ; This formula calculates the total fuzzy project duration. By analyzing the critical path Fuzzy duration of all reinforcement processes The summation is obtained by applying the principle of fuzzy extension, which handles uncertainty through the convolution of membership functions. The formula contains... The fuzzy duration of reinforcement process k in mode l is represented by a six-point fuzzy number. This represents the set of reinforcement operations on the critical path; it is an existing parameter, but used here in a fuzzy context. This formula is used to assess the overall uncertainty of the project duration and is directly connected to the cost and quality objective functions, as variations in duration affect resource and quality requirements.

[0070] The cost objective function is also improved through fuzzification. It should be noted that the relationship between cost and time in actual engineering projects may exhibit various forms, such as linear, quadratic, or exponential. This invention chooses a quadratic function for modeling, primarily based on its practicality and rationality in engineering optimization: a quadratic function is a continuously differentiable convex or concave function, which facilitates the convergence of mathematical optimization algorithms; simultaneously, it can effectively characterize the phenomenon of marginal cost changing over time (for example, when fine-tuning near the normal timeframe, cost changes smoothly, while when the timeframe is drastically compressed or extended, cost increases or decreases rapidly), providing a smooth and easily tractable approximate relationship for multi-objective optimization.

[0071] direct cost function Represented as: ; in, These are direct costs; This is the longest duration of the reinforcement process; The reinforcement process is the longest duration The underlying direct cost is an existing parameter, but it has changed from deterministic to fuzzy context; It is the direct cost increase rate, calculated as ,in, The reinforcement process is carried out in the shortest possible time. The direct costs, these parameters are determined through cost-schedule analysis of the reinforcement process, within the normal construction period. The cost is (Normal cost) within the shortest construction period The cost is (Cost of rushing to complete the project), through two points ( , )and( , Fitting linear or quadratic relationships; It is the fuzzy duration, and The square of the difference reflects the fuzzy impact of time variations on cost. The formula models the nonlinear relationship between cost and time using a quadratic function, and the newly introduced fuzzy computation allows cost calculation to accommodate uncertainty. This formula follows the fuzzy indirect cost function below, as both together constitute the total cost.

[0072] Similarly, this invention also uses a quadratic function to model the relationship between indirect costs and project duration. This approach is primarily based on the common understanding in engineering management: indirect costs (such as project management fees and site facility rental fees) are generally positively correlated with the total project duration, but their growth is not a simple linear relationship. For example, when the project duration is moderately shortened, the cost-saving effect of indirect costs may not be significant; while when the project duration is excessively extended, the complexity of management and coordination may increase significantly, leading to accelerated growth of indirect costs. The quadratic function model can smoothly characterize this non-linear characteristic and maintains mathematical consistency with the direct cost model, facilitating unified processing in optimization algorithms.

[0073] Indirect cost function for: ; in, It is an indirect cost. The reinforcement process is carried out in the shortest possible time. The underlying indirect costs are an existing parameter. It is the indirect cost reduction rate, calculated as ,in These are the indirect costs of the reinforcement process over its longest duration; these parameters are derived from project management cost data. The squared term represents the fuzzy impact of time reduction on indirect costs, as indirect costs (such as administrative expenses) typically increase with shorter project durations. This formula is connected to the total cost calculation, providing input for subsequent optimization. The total fuzzy cost is: ; This formula aggregates the fuzzy direct and indirect costs of all reinforcement processes to obtain the total fuzzy cost of the project. The summation operation involves the addition of fuzzy numbers, handling uncertainty based on the extension principle to ensure the robustness of cost estimation. A cost objective is used in multi-objective optimization and connected to the quality objective function, as cost constraints can influence quality decisions. The quality objective function is also improved accordingly, characterizing the reliability of the overall project quality under different construction schemes.

[0074] This invention uses fuzzy confidence level to measure quality. First, a fuzzy confidence level is defined for each reinforcement step. This invention proposes that the quality of the reinforcement process is closely related to the construction period, and that there exists an optimal construction period range. ,in It is the shortest and optimal construction period. This represents the longest and optimal construction period. When the construction period falls within this range, the quality of the reinforcement process is highest; a construction period that is too short (rushing) or too long (inefficient) will reduce the reliability of the quality. Therefore, Defined as a fuzzy number, its membership function It can be constructed as a triangular or trapezoidal function, with its peak corresponding to a high confidence level (e.g., close to 1.0) within the optimal time interval, decreasing towards both sides; the optimal time interval for each reinforcement process. Determined based on construction quotas, historical project data, or expert experience.

[0075] Furthermore, the overall fuzzy quality of the project It is formed by aggregating the fuzzy reliability of all reinforcement processes. In reliability theory, the total reliability can be modeled as a cascaded module of the reliability of all its units (each reinforcement process). Therefore, the total fuzzy quality... The calculation formula is: ; This formula represents a pessimistic approach, where the final quality of a project is determined by the least reliable stage among all reinforcement processes (the "weakest link" effect). This is a classic approach in reliability models.

[0076] To handle comparisons and sorting between fuzzy numbers, this scheme employs a centroid-based defuzzification method.

[0077] It should be noted that in conventional centroid-based deblurring, only the abscissa of the centroid is calculated. The central tendency of the fuzzy number is used for scalarization, but this scheme additionally defines the centroid ordinate. And correlate it with the x-coordinate of the centroid. Using the Euclidean distance L as a ranking metric is primarily to more comprehensively represent the information of fuzzy numbers, because relying solely on the centroid abscissa is insufficient. It is impossible to distinguish between fuzzy numbers with different shapes but the same center position (such as fuzzy numbers with high uncertainty being "short and fat" and those with high certainty being "tall and thin"). The horizontal coordinate of the centroid may be different. Same). Centroid ordinate Its calculation formula amplifies the influence of high membership regions, essentially reflecting the determinism or concentration of fuzzy numbers. This allows for automatic trade-offs of "performance" (such as schedule or cost) during the optimization process when using Euclidean distance L for sorting, by minimizing the centroid's horizontal coordinate. Pursuit) and "risk" (such as certainty, by maximizing the centroid's vertical axis) This preference naturally leads to a preference for construction schemes with lower risk and more reliable predictions, thus enhancing the practicality and robustness of schedule management.

[0078] First, it is necessary to calculate the fuzzy number. The coordinates of the centroid ( , The calculation formula is as follows: ; ; in, The horizontal axis represents the centroid, indicating the average position of the fuzzy number in the domain (such as time or cost). It is the centroid ordinate, which reflects the overall distribution height of the membership function and is determined by the shape of the function; It is a fuzzy number The membership function, for the six-point fuzzy number defined in this paper. Its membership function is a piecewise linear function, therefore the barycenter x-coordinate with centroid ordinate The definite value can be obtained by analytical solution through piecewise integration.

[0079] Subsequently, the coordinates of the centroid were calculated. , The Euclidean distance L from the origin (0,0) to the coordinate system is used as the sorting criterion: ; The smaller the Euclidean distance L value, the shorter the time or the lower the cost represented by the fuzzy number (because its center of gravity is closer to the origin), and thus it is used in optimization algorithms to perform scalar comparison and preference selection of fuzzy objective function values.

[0080] A multi-objective optimization algorithm is used to solve the model, ultimately obtaining a Pareto optimal solution set. The construction team can select a satisfactory optimized construction sequence from this set based on the current project priorities. This sequence is the result of a global optimization adjustment of the original recommended construction sequence, taking into account fuzzy time, fuzzy cost, fuzzy quality, and inter-process logical constraints. First, the solution set is stratified using non-dominated sorting, and then the crowding distance is calculated to maintain population diversity. The formula for calculating the crowding distance is: ; in, This represents the value of the i-th solution on the m-th objective function; and Let represent the maximum and minimum values ​​of the m-th objective function, respectively. The algorithm evolves the population through selection, crossover, and mutation operations, dynamically adjusting the weight distribution of the objective function to avoid using fixed, subjective weights. This improvement enables the model to adaptively handle changes in objective priority at different construction stages.

[0081] Taking a certain reinforcement process as an example, the derivation process includes the following steps: First, the NSGA-II algorithm is used to initialize the population, randomly generating chromosomes representing the reinforcement process sequence and duration. Next, non-dominated sorting is performed, and individual fitness is calculated based on fuzzy objective functions such as time, cost, and quality. The population is then stratified, and crowding within each non-dominated stratum is calculated to maintain distribution. Subsequently, the population is evolved through genetic operations, with appropriate crossover and mutation probabilities set. The algorithm iterates until convergence, ultimately outputting a Pareto optimal solution set. Satisfactory solutions can be selected from this set based on actual preferences (such as the shortest construction period).

[0082] After obtaining the optimized construction sequence, it is necessary to further develop an executable schedule optimization plan for the reinforcement procedures. This plan must clearly define the specific time arrangement for each reinforcement procedure under the optimized sequence. Specifically, based on the fuzzy start time parameters for each procedure obtained from the model (e.g., the optimized fuzzy start time for a certain procedure is [10, 15, 21, 28, 31, 38] days), and combined with on-site resource conditions and calendar time, the specific start time window, resource allocation plan, and key control nodes for each procedure should be determined. The construction team can then dynamically adjust the work plan based on this plan and the actual on-site conditions (such as weather changes and resource availability).

[0083] The optimized construction sequence and the corresponding schedule optimization plan are fed back to the construction team for execution, and construction impact data (such as actual construction period and resource consumption) are collected in real time for model iteration. For example, the upper bound of the fuzzy time parameter is dynamically corrected using actual construction period deviation data, and the optimization algorithm is rerun to generate an updated optimization sequence and plan, forming a closed-loop control mechanism.

[0084] The core function of this step is to construct an optimization model for reinforcement procedures using the critical path method when the duration of interference risks exceeds the construction plan data. This model transforms the recommended construction sequence into an optimized one, and based on this, forms a specific schedule optimization plan for the reinforcement procedures, thereby achieving proactive risk management of the construction process. This step not only provides a scientific basis for construction decisions in high-risk scenarios, ensuring the optimal allocation of resources and schedule, but also lays the foundation for dynamic adjustment of subsequent construction execution and monitoring. Through real-time feedback and impact data collection, a closed-loop control mechanism is formed, effectively supporting the continuous optimization of construction plans and risk early warning, and improving the adaptability and reliability of overall project management.

[0085] This solution proposes a method and system for managing the construction progress of existing building reinforcement projects. Addressing the challenge of traditional experience-based progress management being ill-suited to dynamic risks, this solution collects multi-source data on structural health and construction impacts. It employs a fuzzy comprehensive evaluation method combining Analytic Hierarchy Process (AHP) and entropy weighting to quantify structural deterioration and functional disturbance risks. Construction sequence is then prioritized based on risk, and by comparing the duration of disturbance risks with permissible delays, a suitable phased or optimized schedule is generated using the critical path method and a multi-objective optimization model. This achieves precise risk control and efficient schedule optimization, enhancing the scientific rigor and adaptability of management.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, the phrase "comprising an element defined as..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

Claims

1. A method for managing the construction progress of reinforcement of existing buildings and structures, characterized in that, Includes the following steps: Step S1: Collect structural health data, construction impact data, meteorological environment data, operational disturbance data, and construction plan data of existing buildings and structures; Step S2: Use fuzzy comprehensive evaluation method to analyze the structural health data, construction impact data, and meteorological environment data, and calculate the structural deterioration risk tendency degree; The fuzzy comprehensive evaluation method is used to analyze the operational interference data, construction impact data, and construction plan data to calculate the functional interference risk tendency; and the comprehensive risk tendency is calculated by combining the structural deterioration risk tendency and the functional interference risk tendency. Step S3: When the comprehensive risk tendency is greater than the preset threshold, calculate and sort the construction priority recommendation index of each reinforcement process based on the structural deterioration risk tendency and functional interference risk tendency to obtain the recommended construction sequence. Step S4: Collect historical project data, calculate the weighted average risk index based on the recommended construction sequence, and obtain the duration of interference risk by combining the historical project data. Compare the duration of interference risk with the allowable delay duration in the construction plan data. When the duration of interference risk is less than the allowable delay duration, implement the phased schedule according to the recommended construction sequence. Step S5: When the duration of the interference risk exceeds the allowable delay duration, construct a reinforcement process optimization model based on the critical path algorithm, input the recommended construction sequence into the reinforcement process optimization model, output the optimized construction sequence, and then implement the progress optimization scheme of the reinforcement process according to the optimized construction sequence.

2. The method for managing the construction progress of reinforcement of existing buildings and structures according to claim 1, characterized in that, Collect structural health data, construction impact data, meteorological environmental data, operational disturbance data, and construction plan data of existing buildings and structures, including: The multi-source heterogeneous data of existing buildings and structures mainly includes five categories, namely structural health data. Construction impact data Meteorological and environmental data Operational interference data and construction plan data ; Structural health data It is the core of assessing the safety status of a building or structure, and its acquisition depends on on-site investigation and testing; construction impact data The direct effects of reinforcement work on the structure are described, primarily through construction plans and real-time monitoring; meteorological environmental data. This refers to variable factors from the external environment, primarily obtained by connecting to local meteorological monitoring networks or deploying miniature weather stations on-site; operational interference data. This reflects the constraints that buildings impose on construction during their use, stemming from research on the owner's operational model; construction plan data. It is the baseline of project management, provided by the project management documents.

3. The method for managing the construction progress of reinforcement of existing buildings and structures according to claim 2, characterized in that, The structural health data, construction impact data, and meteorological environment data were analyzed using the fuzzy comprehensive evaluation method to calculate the structural deterioration risk tendency. The fuzzy comprehensive evaluation method was used to analyze the operational interference data, construction impact data, and construction plan data to calculate the functional interference risk tendency degree, including: Construct a factor set U and an evaluation set V, and construct a fuzzy relation matrix R. The elements in matrix R... The degree to which index i belongs to evaluation level j is represented by a semi-trapezoidal membership function. For each indicator, based on its numerical value, its membership degree to each risk level is calculated using a preset membership function. Fuzzy synthesis is then performed to obtain the evaluation result B, where each component of B is an element. The calculation formula is as follows: ； in, For comprehensive weighting; For fuzzy operators; This represents the overall membership degree to level v; The fuzzy relation matrix R represents the membership degree of index i to evaluation level v; M is the number of evaluation indicators; and v is the risk level number. Structural deterioration risk tendency Calculated by weighted average: ； in, It is the membership degree of the v-th level in B. It is the assignment of level v. For the number of levels, The value represents the tendency of structural deterioration risk; the higher the value, the more significant the structural deterioration risk. Risk tendency for functional interference According to the degree of structural deterioration risk The same fuzzy comprehensive evaluation calculation method is used to calculate the structural degradation risk tendency degree. The input data is replaced with operational interference data, construction impact data, and construction plan data, while the remaining calculation steps remain unchanged.

4. The method for managing the construction progress of reinforcement of existing buildings and structures according to claim 3, characterized in that, Combining the structural degradation risk tendency and the functional disturbance risk tendency, a comprehensive risk tendency is calculated, including: Overall risk tendency By structural deterioration risk tendency and functional interference risk tendency The linear weighted sum is calculated using the following formula: ； in, It is the overall risk tendency; It is the structural degradation risk tendency obtained from the aforementioned calculation; It is the functional interference risk tendency degree calculated above; α and β are respectively and The combined weighting coefficients.

5. A method for managing the construction progress of reinforcement of existing buildings and structures according to claim 4, characterized in that, When the overall risk tendency is greater than a preset threshold, the construction priority recommendation index of each reinforcement process is calculated and ranked based on the structural deterioration risk tendency and functional interference risk tendency to obtain the recommended construction sequence, including: Comprehensive risk orientation of existing buildings and structures When the threshold T is exceeded, the construction priority recommendation index for each reinforcement process is calculated. To optimize the construction sequence; Perform a threshold condition check only if the overall risk tendency is... Subsequent calculations are only triggered when the value exceeds a preset threshold T. Based on the threshold condition being met, a construction priority recommendation index needs to be calculated for each construction reinforcement procedure k. The calculation formula is: ； in, This represents the priority recommendation index for reinforcement procedure k. It is the degree of structural deterioration risk tendency; It is the degree of functional interference risk tendency; and These are the weighting coefficients of reinforcement process k on the risks of structural deterioration and functional disruption, respectively. The construction priority recommendation index for all procedures was calculated. Then, the sorting process needs to consider the logical dependencies between processes, and a process dependency matrix D is preset; The recommended construction sequence is obtained by sorting the components according to the process dependency matrix D.

6. The method for managing the construction progress of reinforcement of existing buildings and structures according to claim 5, characterized in that, The duration of the disruption risk is compared with the permissible delay duration in the construction plan data. When the duration of the disruption risk is less than the permissible delay duration, a phased schedule is implemented according to the recommended construction sequence, including: The weighted average risk index R is calculated based on the recommended construction sequence, using the following formula: ； Where R represents the weighted average risk index; It is an index of reinforcement processes; This represents the total number of reinforcement procedures. The construction priority recommendation index for reinforcement process k; D represents the estimated duration of reinforcement process k; D represents the total construction duration; weighting coefficients. This indicates the proportion of reinforcement process k in the total construction period; After calculating the weighted average risk index, it needs to be converted into an operational duration indicator, namely, the duration of disturbance risk. It can be calculated using the following formula: ； in, This indicates the duration of the interference risk; R is the weighted average risk index obtained above; This is the risk-time conversion factor.

7. A method for managing the construction progress of reinforcement of existing buildings and structures according to claim 6, characterized in that, When the duration of the interference risk exceeds the permitted delay duration, an optimization model for the hardening process is constructed based on the critical path algorithm, including: Construct an optimization model based on the fuzzy critical path method, output the optimal construction sequence, and formulate a specific schedule optimization plan for the reinforcement process accordingly. By introducing fuzzy mathematics theory, and replacing deterministic parameters with six-point fuzzy numbers, the duration of reinforcement process i is represented by a fuzzy number. The total fuzzy project duration is calculated based on fuzzy numbers. ; The cost objective function is also fuzzified to obtain the direct cost function. and indirect cost function Then calculate the total fuzzy cost. ; Quality is measured using fuzzy confidence level, and a fuzzy confidence level is preset for each reinforcement process. The overall fuzzy quality of the project The total fuzzy quality is obtained by aggregating the fuzzy confidence levels of all reinforcement processes. ; The defuzzification method based on the centroid method requires the calculation of fuzzy numbers. The coordinates of the centroid ( , The calculation formula is as follows: ； ； in, It is the x-coordinate of the centroid; It is the ordinate of the centroid; It is a fuzzy number Membership function; Calculate the coordinates of the centroid ( , The Euclidean distance L from the origin (0,0) to the coordinate system. Based on the Euclidean distance L, the centroid coordinates are converted into scalar values, and a multi-objective optimization algorithm is used to solve the model, finally obtaining a Pareto optimal solution set.

8. A method for managing the construction progress of reinforcement of existing buildings and structures according to claim 7, characterized in that, The recommended construction sequence is input into the reinforcement process optimization model, and the optimized construction sequence is output, including: The NSGA-II algorithm is used to initialize the population, and chromosomes representing the reinforcement process sequence and duration are randomly generated. Then, non-dominated sorting is performed, and the fitness of individuals is calculated based on the fuzzy objective functions of construction period, cost and quality. The population is then stratified, and the crowding degree of individuals in each non-dominated layer is calculated to maintain the distribution. The population is evolved through genetic operations, and the corresponding crossover probability and mutation probability are set. The algorithm is iteratively run until convergence, and finally, the Pareto optimal solution set is output. Satisfactory solutions can be selected from the solution set based on actual preferences.

9. A method for managing the construction progress of reinforcement of existing buildings and structures according to claim 8, characterized in that, The progress optimization scheme for the reinforcement process, based on the optimized construction sequence, includes: After obtaining the optimized construction sequence, it is necessary to further develop an executable schedule optimization plan for the reinforcement process. This plan should specify the specific time arrangement of each reinforcement process under the optimized sequence. That is, based on the fuzzy start time parameters of each process obtained by model solving, combined with the site resource conditions and calendar time, the specific start time window, resource allocation plan and key control nodes of each process should be determined. The construction party can dynamically adjust the work plan according to this plan and the actual site conditions.

10. A construction progress management system for the reinforcement of existing buildings and structures, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-9.

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